Artificial intelligence in environmental monitoring: in-depth analysis
Abstract This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations s...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-11-01
|
| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-024-00198-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849221106635898880 |
|---|---|
| author | Emran Alotaibi Nadia Nassif |
| author_facet | Emran Alotaibi Nadia Nassif |
| author_sort | Emran Alotaibi |
| collection | DOAJ |
| description | Abstract This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the “black-box” nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges. |
| format | Article |
| id | doaj-art-bf18dea50eff4ef5a63bad9a18b80d27 |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-bf18dea50eff4ef5a63bad9a18b80d272024-11-24T12:35:34ZengSpringerDiscover Artificial Intelligence2731-08092024-11-014112610.1007/s44163-024-00198-1Artificial intelligence in environmental monitoring: in-depth analysisEmran Alotaibi0Nadia Nassif1Department of Civil and Environmental Engineering, Khalifa University of Science and TechnologyDepartment of Civil and Environmental Engineering, University of SharjahAbstract This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the “black-box” nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges.https://doi.org/10.1007/s44163-024-00198-1Deep learning modelsBig data analyticsIoT-enabled monitoringExplainable AIRemote sensing technologiesSustainable ecosystem management |
| spellingShingle | Emran Alotaibi Nadia Nassif Artificial intelligence in environmental monitoring: in-depth analysis Discover Artificial Intelligence Deep learning models Big data analytics IoT-enabled monitoring Explainable AI Remote sensing technologies Sustainable ecosystem management |
| title | Artificial intelligence in environmental monitoring: in-depth analysis |
| title_full | Artificial intelligence in environmental monitoring: in-depth analysis |
| title_fullStr | Artificial intelligence in environmental monitoring: in-depth analysis |
| title_full_unstemmed | Artificial intelligence in environmental monitoring: in-depth analysis |
| title_short | Artificial intelligence in environmental monitoring: in-depth analysis |
| title_sort | artificial intelligence in environmental monitoring in depth analysis |
| topic | Deep learning models Big data analytics IoT-enabled monitoring Explainable AI Remote sensing technologies Sustainable ecosystem management |
| url | https://doi.org/10.1007/s44163-024-00198-1 |
| work_keys_str_mv | AT emranalotaibi artificialintelligenceinenvironmentalmonitoringindepthanalysis AT nadianassif artificialintelligenceinenvironmentalmonitoringindepthanalysis |